Abstract:
Differential Evolution (DE) is a popular
efficient population-based stochastic optimization
technique for solving real-world optimization
problems in various domains. In knowledge discovery
and data mining, optimization-based pattern
recognition has become an important field, and
optimization approaches have been exploited to
enhance the efficiency and accuracy of classification,
clustering and association rule mining. Like other
population-based approaches, the performance of DE
relies on the positions of initial population which may
lead to the situation of stagnation and premature
convergence. This paper describes a differential
evolution algorithm for solving clustering problems,
in which opposition-based learning (OBL) is utilized
to create high-quality solutions for initial population,
and enhance the performance of clustering. The
experimental test has been carried out on some UCI
standard datasets that are mostly used for
optimization-based clustering. According to the
results, the proposed algorithm is more efficient and
robust than classical DE based clustering.
